Chaotic frequency scaling in a coupled oscillator model for free rhythmic actions

The question of how best to model rhythmic movements at self-selected amplitude-frequency combinations, and their variability, is a long-standing issue. This study presents a systematic analysis of a coupled oscillator system that has successfully accounted for the experimental result that humans' preferred oscillation frequencies closely correspond to the linear resonance frequencies of the biomechanical limb systems, a phenomenon known as resonance tuning or frequency scaling. The dynamics of the coupled oscillator model is explored by numerical integration in different areas of its parameter space, where a period doubling route to chaotic dynamics is discovered. It is shown that even in the regions of the parameter space with chaotic solutions, the model still effectively scales to the biomechanical oscillator's natural frequency. Hence, there is a solution providing for frequency scaling in the presence of chaotic variability. The implications of these results for interpreting variability as fundamentally stochastic or chaotic are discussed.     

Key technologies for IMT-advanced mobile communication systems

WINNER is an ambitious research project aiming at identification, development, and assessment of key technologies for IMTAdvanced mobile communication systems. WINNER has devised an OFDMA-based system concept with excellent system-level performance for flexible deployments in a wide variety of operating conditions. The WINNER system provides a significant step forward from current 3G systems. Key innovations integrated into the system concept include flexible spectrum usage and relaying, adaptive advanced antenna schemes and pilot design, close to optimal link adaptation, hierarchical control signaling, and a highly flexible multiple access scheme. The end-to-end performance assessment results demonstrate that the WINNER concept meets the IMTAdvanced requirements.     

Wiener filter design using polynomial equations

A simplified way of deriving realizable and explicit Wiener filters is presented. Discrete-time problems are discussed in a polynomial equation framework. Optimal filters, predictors, and smoothers are calculated by means of spectral factorizations and linear polynomial equations. A tool for obtaining these equations, for a given problem structure, is described. It is based on the evaluation of orthogonality in the frequency domain, by means of canceling stable poles with zeros. Comparisons are made to previously known derivation methodologies such as completing the squares for the polynomial systems approach and the classical Wiener solution. The simplicity of the proposed derivation method is particularly evident in multistage filtering problems. To illustrate, two examples are discussed: a filtering and a generalized deconvolution problem. A new solvability condition for linear polynomial equation appearing in scalar problems is also presented     

Variability, covariation, and invariance with respect to coordinate systems in motor control: Reply to Smeets and Louw (2007).

In their comment on the tolerance-noise covariation (TNC) method for decomposing variability by H. Müller and D. Sternad (2003, 2004b), J. B. J. Smeets and S. Louw (see record 2007-01135-017) show that covariation (C), as defined within the TNC method, is not invariant with respect to coordinate transformations and contend that it is, therefore, meaningless. Although the observation is correct, their interpretation is misleading in the following ways: (a) They equate covariation C with the known statistical quantity covariance and noise (N) with standard deviations. The two quantities C and N are conceptually different statistical measures. (b) Dependency on the reference frame is not only a feature of C but of all 3 components. However, such dependency is ubiquitous in motor control. (c) As the frame of reference in biological systems is poorly understood, the TNC method may afford evaluation of different coordinates for control. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Adaptive modulation systems for predicted wireless channels

When adaptive modulation is used to counter short-term fading in mobile radio channels, signaling delays create problems with outdated channel state information. The use of channel power prediction will improve the performance of the link adaptation. It is then of interest to take the quality of these predictions into account explicitly when designing an adaptive modulation scheme. We study the optimum design of an adaptive modulation scheme based on uncoded M-quadrature amplitude modulation, assisted by channel prediction for the flat Rayleigh fading channel. The data rate, and in some variants the transmit power, are adapted to maximize the spectral efficiency, subject to average power and bit-error rate constraints. The key issues studied here are how a known prediction error variance will affect the optimized transmission properties, such as the signal-to-noise ratio (SNR) boundaries that determine when to apply different modulation rates, and to what extent it affects the spectral efficiency. This investigation is performed by analytical optimization of the link adaptation, using the statistical properties of a particular, but efficient, channel power predictor. Optimum solutions for the rate and transmit power are derived, based on the predicted SNR and the prediction error variance.     

Tracking of time-varying mobile radio channels. II. A case study

For pt.I see ibid., vol.49, p.2207-17 (2001). Low-complexity Wiener LMS (WLMS) adaptation algorithms, of use for channel estimation, have been derived in Lindbom et al. (2001). They are here evaluated on the fast fading radio channels encountered in IS-136 TDMA systems, with the aim of clarifying several issues: How much can channel estimation performance be improved with these tools, as compared to LMS adaptation? When can an improved tracking MSE be expected to result in a meaningful reduction of the bit error rate? Will optimal prediction of future channel estimates significantly improve the equalization? Can one single tracker with fixed gain be used for all encountered Doppler frequencies and SNRs, or must a more elaborate scheme be adopted? These questions are here investigated both analytically and by simulation. An exact analytical expression for the tracking MSE on two-tap FIR channels is presented and utilized. With this tool, the MSE performance and robustness of WLMS algorithms based on different statistical models can be investigated. A simulation study then compares the uncoded bit error rate of detectors, where channel trackers are used in decision directed mode in conjunction with Viterbi algorithms. A Viterbi detector combined with WLMS, based on second order autoregressive fading models possibly combined with integration, provides good performance and robustness at a reasonable complexity     

Optimal differentiation based on stochastic signal models

The problem of estimating the time derivative of a signal from sampled measurements is addressed. The measurements may be corrupted by colored noise. A key idea is to use stochastic models of the signal to be differentiated and of the measurement noise. Two approaches are suggested. The first is based on a continuous-time stochastic process as a model of the signal. The second uses a discrete-time ARMA model of the signal and a discrete-time approximation of the derivative operator. Digital differentiators are presented in a shift operator polynomial form. They minimize the mean-square estimation error, and are calculated from a linear polynomial equation and a polynomial spectral factorization. The three obstacles to perfect differentiation, namely a finite smoothing lag, measurement noise, and aliasing effects due to sampling, are discussed     

Wiener design of adaptation algorithms with time-invariant gains

A design method is presented that extends least mean squared (LMS) adaptation of time-varying parameters by including general linear time-invariant filters that operate on the instantaneous gradient vector. The aim is to track time-varying parameters of linear regression models in situations where the regressors are stationary or have slowly time-varying properties. The adaptation law is optimized with respect to the steady-state parameter error covariance matrix for time-variations modeled as vector-ARIMA processes. The design method systematically uses prior information about time-varying parameters to provide filtering, prediction, or fixed lag smoothing estimates for arbitrary lags. The method is based on a transformation of the adaptation problem into a Wiener filter design problem. The filter works in open loop for slow parameter variations, whereas a time-varying closed loop has to be considered for fast variations. In the latter case, the filter design is performed iteratively. The general form of the solution at each iteration is obtained by a bilateral Diophantine polynomial matrix equation and a spectral factorization. For white gradient noise, the Diophantine equation has a closed-form solution. Further structural constraints result in very simple design equations. Under certain model assumptions, the Wiener designed adaptation laws reduce to LMS adaptation. Compared with Kalman estimators, the channel tracking performance becomes nearly the same in mobile radio applications, whereas the complexity is, in general, much lower     

Development of an automated wood welding process

Friction welding of wood is an active field of research and would seem to be a potential joining technology in wood industries in the near future. Despite numerous scientific publications in this field, automated industrial applications of this technique are not common up to now. In this paper, we developed an automated welding process that can be easily implemented in industry. The use of beech wood samples is motivated because (i) this species is abundant in the forest resource, (ii) the anatomical structure is homogeneous and therefore highly suitable for the welding process and (iii) the average wood density is high. For the welding process, a sequential control was developed and four different welding modes were applied. We tested four welding modes with a constant rotation speed of the drilling machine (1800?t.mn). During the dowel insertion, the linear displacement was tested for two different constant speeds and for two varying speeds. The results of the pull-out strength test show that the forces during the welding process as well as the strength of the joints produced differ depending on the welding mode. Based on the results it can be recommended that a two-step welding process is applied for wood welding. With the two steps, the forces at the welding machine are kept low and the quality of the joint is high. The sequential control developed can be applied and adapted for different industrial applications. These findings should convince industrial decision-makers of the applicability of this process for daily production.     

Tracking of time-varying mobile radio channels .1. The Wiener LMSalgorithm

Adaptation algorithms with constant gains are designed for tracking smoothly time-varying parameters of linear regression models, in particular channel models occurring in mobile radio communications. In a companion paper, an application to channel tracking in the IS-136 TDMA system is discussed. The proposed algorithms are based on two key concepts. First, the design is transformed into a Wiener filtering problem. Second, the parameters are modeled as correlated ARIMA processes with known dynamics. This leads to a new framework for systematic and optimal design of simple adaptation laws based on prior information. The algorithms can be realized as Wiener filters, called learning filters, or as "LMS/Newton" updates complemented by filters that provide predictions or smoothing estimates. The simplest algorithm, named the Wiener LMS, is presented. All parameters are here assumed governed by the same dynamics and the covariance matrix of the regressors is assumed known. The computational complexity is of the same order of magnitude as that of LMS for regressors which are either white or have autoregressive statistics. The tracking performance is, however, substantially improved